Health system resilience in Nigeria after Ebola and COVID-19: impacts, improvements, and strategic directions
Bibliographic record
Abstract
Nigeria's healthcare system was severely challenged by the 2014 Ebola Virus Disease (EVD) outbreak and the 2020 COVID-19 pandemic. These events exposed systemic weaknesses in infrastructure, emergency preparedness, and health service delivery, while also prompting improvements in diagnostics, surveillance, and public health coordination. This paper analyzes the impacts of the COVID-19 and EVD outbreaks on Nigeria’s health system, as well as the advancements achieved during and after the crises. This review utilizes publicly available articles from sources such as Google Scholar, PubMed, and other grey literature from NCDC and WHO, among others. The search was focused on the Nigerian healthcare system, Ebola and COVID-19 outbreaks. The Ebola and COVID-19 outbreaks led to major disruptions in Nigeria’s healthcare system, including declines in antenatal care, immunization coverage, tuberculosis and HIV services, and in-facility deliveries. During the Ebola outbreak, emergency operations centers and digital surveillance systems like SORMAS were implemented to strengthen outbreak response. The COVID-19 pandemic prompted the adoption of telemedicine, expanded molecular diagnostic capacity, and large-scale investments in health infrastructure, enhancing service delivery beyond pandemic-specific needs. Both outbreaks disrupted essential healthcare services but also spurred critical investments and innovations. Strengthening health system resilience requires sustained funding, institutional reforms, and the integration of emergency gains into routine healthcare. The paper recommends that the healthcare system capacity and surveillance system should be strengthened, and there should be full adoption of telemedicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".